The Wallace H. Coulter Department of Biomedical Engineering

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

Electrospinning is a versatile and viable technique for generating ultrathin fibers. Remarkable progress has been made with regard to the development of electrospinning methods and engineering of electrospun nanofibers to suit or enable various applications. We aim to provide a comprehensive overview of electrospinning, including the principle, methods, materials, and applications. We begin with a brief introduction to the early history of electrospinning, followed by discussion of its principle and typical apparatus. We then discuss its renaissance over the past two decades as a powerful technology for the production of nanofibers with diversified compositions, structures, and properties. Afterward, we discuss the applications of electrospun nanofibers, including their use as "smart" mats, filtration membranes, catalytic supports, energy harvesting/conversion/storage components, and photonic and electronic devices, as well as biomedical scaffolds. We highlight the most relevant and recent advances related to the applications of electrospun nanofibers by focusing on the most representative examples. We also offer perspectives on the challenges, opportunities, and new directions for future development. At the end, we discuss approaches to the scale-up production of electrospun nanofibers and briefly discuss various types of commercial products based on electrospun nanofibers that have found widespread use in our everyday life.

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

In medicine, nanotechnology has sparked a rapidly growing interest as it promises to solve a number of issues associated with conventional therapeutic agents, including their poor water solubility (at least, for most anticancer drugs), lack of targeting capability, nonspecific distribution, systemic toxicity, and low therapeutic index. Over the past several decades, remarkable progress has been made in the development and application of engineered nanoparticles to treat cancer more effectively. For example, therapeutic agents have been integrated with nanoparticles engineered with optimal sizes, shapes, and surface properties to increase their solubility, prolong their circulation half-life, improve their biodistribution, and reduce their immunogenicity. Nanoparticles and their payloads have also been favorably delivered into tumors by taking advantage of the pathophysiological conditions, such as the enhanced permeability and retention effect, and the spatial variations in the pH value. Additionally, targeting ligands (e.g., small organic molecules, peptides, antibodies, and nucleic acids) have been added to the surface of nanoparticles to specifically target cancerous cells through selective binding to the receptors overexpressed on their surface. Furthermore, it has been demonstrated that multiple types of therapeutic drugs and/or diagnostic agents (e.g., contrast agents) could be delivered through the same carrier to enable combination therapy with a potential to overcome multidrug resistance, and real-time readout on the treatment efficacy. It is anticipated that precisely engineered nanoparticles will emerge as the next-generation platform for cancer therapy and many other biomedical applications.

We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes.

The task of eukaryotic genome annotation remains challenging. Only a few genomes could serve as standards of annotation achieved through a tremendous investment of human curation efforts. Still, the correctness of all alternative isoforms, even in the best-annotated genomes, could be a good subject for further investigation. The new BRAKER2 pipeline generates and integrates external protein support into the iterative process of training and gene prediction by GeneMark-EP+ and AUGUSTUS. BRAKER2 continues the line started by BRAKER1 where self-training GeneMark-ET and AUGUSTUS made gene predictions supported by transcriptomic data. Among the challenges addressed by the new pipeline was a generation of reliable hints to protein-coding exon boundaries from likely homologous but evolutionarily distant proteins. In comparison with other pipelines for eukaryotic genome annotation, BRAKER2 is fully automatic. It is favorably compared under equal conditions with other pipelines, e.g. MAKER2, in terms of accuracy and performance. Development of BRAKER2 should facilitate solving the task of harmonization of annotation of protein-coding genes in genomes of different eukaryotic species. However, we fully understand that several more innovations are needed in transcriptomic and proteomic technologies as well as in algorithmic development to reach the goal of highly accurate annotation of eukaryotic genomes.

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.

Achieving mastery over the synthesis of metal nanocrystals has emerged as one of the foremost scientific endeavors in recent years. This intense interest stems from the fact that the composition, size, and shape of nanocrystals not only define their overall physicochemical properties but also determine their effectiveness in technologically important applications. Our aim is to present a comprehensive review of recent research activities on bimetallic nanocrystals. We begin with a brief introduction to the architectural diversity of bimetallic nanocrystals, followed by discussion of the various synthetic techniques necessary for controlling the elemental ratio and spatial arrangement. We have selected key examples from the literature that exemplify critical concepts and place a special emphasis on mechanistic understanding. We then discuss the composition-dependent properties of bimetallic nanocrystals in terms of catalysis, optics, and magnetism and conclude the Review by highlighting applications that have been enabled and/or enhanced by precisely controlling the synthesis of bimetallic nanocrystals.

MOTIVATION: Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. RESULTS: We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. AVAILABILITY AND IMPLEMENTATION: BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ CONTACT: katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The International Strawberry Sequencing Consortium reports the draft genome of the woodland strawberry (Fragaria vesca). The genome of this diploid species should serve as a reference genome for the Fragaria genus, as the cultivated strawberry (Fragaria × ananassa) is an octoploid where F. vesca is predicted to be a subgenome donor. The woodland strawberry, Fragaria vesca (2n = 2x = 14), is a versatile experimental plant system. This diminutive herbaceous perennial has a small genome (240 Mb), is amenable to genetic transformation and shares substantial sequence identity with the cultivated strawberry (Fragaria × ananassa) and other economically important rosaceous plants. Here we report the draft F. vesca genome, which was sequenced to ×39 coverage using second-generation technology, assembled de novo and then anchored to the genetic linkage map into seven pseudochromosomes. This diploid strawberry sequence lacks the large genome duplications seen in other rosids. Gene prediction modeling identified 34,809 genes, with most being supported by transcriptome mapping. Genes critical to valuable horticultural traits including flavor, nutritional value and flowering time were identified. Macrosyntenic relationships between Fragaria and Prunus predict a hypothetical ancestral Rosaceae genome that had nine chromosomes. New phylogenetic analysis of 154 protein-coding genes suggests that assignment of Populus to Malvidae, rather than Fabidae, is warranted.

We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources.

ConspectusElectrospinning is a simple and versatile technique that relies on the electrostatic repulsion between surface charges to continuously draw nanofibers from a viscoelastic fluid. It has been applied to successfully produce nanofibers, with diameters down to tens of nanometers, from a rich variety of materials, including polymers, ceramics, small molecules, and their combinations. In addition to solid nanofibers with a smooth surface, electrospinning has also been adapted to generate nanofibers with a number of secondary structures, including those characterized by a porous, hollow, or core–sheath structure. The surface and/or interior of such nanofibers can be further functionalized with molecular species or nanoparticles during or after an electrospinning process. In addition, electrospun nanofibers can be assembled into ordered arrays or hierarchical structures by manipulation of their alignment, stacking, and/or folding. All of these attributes make electrospun nanofibers well-suited for a broad spectrum of applications, including those related to air filtration, water purification, heterogeneous catalysis, environmental protection, smart textiles, surface coating, energy harvesting/conversion/storage, encapsulation of bioactive species, drug delivery, tissue engineering, and regenerative medicine.Over the past 15 years, our group has extensively explored the use of electrospun nanofibers for a range of applications. Here we mainly focus on two examples: (i) use of ceramic nanofibers as catalytic supports for noble-metal nanoparticles and (ii) exploration of polymeric nanofibers as scaffolding materials for tissue regeneration. Because of their high porosity, high surface area to volume ratio, well-controlled composition, and good thermal stability, nonwoven membranes made of ceramic nanofibers are terrific supports for catalysts based on noble-metal nanoparticles. We have investigated the use of ceramic nanofibers made of various oxides, including SiO2, TiO2, SnO2, CeO2, and ZrO2, as supports for heterogeneous catalysts based on noble metals such as Au, Pt, Pd, and Rh. On the other hand, the diameter, composition, alignment, porosity, and surface properties of polymeric nanofibers can be engineered in a controllable fashion to mimic the hierarchical architecture of an extracellular matrix and help manipulate cell behaviors for tissue engineering and regenerative medicine. To this end, we can mimic the native structure and morphology of the extracellular matrix in tendon using uniaxially aligned nanofibers; we can use radially aligned nanofibers to direct the migration of cells from the periphery to the center in an effort to speed up wound healing; and we can also use uniaxially aligned nanofibers to guide and expedite the extension of neurites for peripheral nerve repair. Furthermore, we can replicate the anatomic structures at the tendon-to-bone insertion using nanofiber scaffolds with graded mineral coatings. In this Account, we aim to demonstrate the unique capabilities of electrospun nanofibers as porous supports for heterogeneous catalysis and as functional scaffolds for tissue regeneration by concentrating on some of the recent results.

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTGold Nanomaterials at Work in BiomedicineXuan Yang†, Miaoxin Yang‡, Bo Pang†, Madeline Vara‡, and Younan Xia*†‡§View Author Information† The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia 30332, United States‡ § ‡School of Chemistry and Biochemistry and §School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States*E-mail: [email protected]Cite this: Chem. Rev. 2015, 115, 19, 10410–10488Publication Date (Web):August 21, 2015Publication History Received31 March 2015Published online21 August 2015Published inissue 14 October 2015https://pubs.acs.org/doi/10.1021/acs.chemrev.5b00193https://doi.org/10.1021/acs.chemrev.5b00193review-articleACS PublicationsCopyright © 2015 American Chemical SocietyRequest reuse permissionsArticle Views28302Altmetric-Citations964LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Gold,Metal clusters,Metal nanoparticles,Nanoparticles,Nanostructures Get e-Alerts

We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.

Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

This article provides a progress report on the use of galvanic replacement for generating complex hollow nanostructures with tunable and well-controlled properties. We begin with a brief account of the mechanistic understanding of galvanic replacement, specifically focused on its ability to engineer the properties of metal nanostructures in terms of size, composition, structure, shape, and morphology. We then discuss a number of important concepts involved in galvanic replacement, including the facet selectivity involved in the dissolution and deposition of metals, the impacts of alloying and dealloying on the structure and morphology of the final products, and methods for promoting or preventing a galvanic replacement reaction. We also illustrate how the capability of galvanic replacement can be enhanced to fabricate nanomaterials with complex structures and/or compositions by coupling with other processes such as co-reduction and the Kirkendall effect. Finally, we highlight the use of such novel metal nanostructures fabricated via galvanic replacement for applications ranging from catalysis to plasmonics and biomedical research, and conclude with remarks on prospective future directions.

Recent advances in soft materials and system integration technologies have provided a unique opportunity to design various types of wearable flexible hybrid electronics (WFHE) for advanced human healthcare and human-machine interfaces. The hybrid integration of soft and biocompatible materials with miniaturized wireless wearable systems is undoubtedly an attractive prospect in the sense that the successful device performance requires high degrees of mechanical flexibility, sensing capability, and user-friendly simplicity. Here, the most up-to-date materials, sensors, and system-packaging technologies to develop advanced WFHE are provided. Details of mechanical, electrical, physicochemical, and biocompatible properties are discussed with integrated sensor applications in healthcare, energy, and environment. In addition, limitations of the current materials are discussed, as well as key challenges and the future direction of WFHE. Collectively, an all-inclusive review of the newly developed WFHE along with a summary of imperative requirements of material properties, sensor capabilities, electronics performance, and skin integrations is provided.

Organs-on-chips (OoCs) are systems containing engineered or natural miniature tissues grown inside microfluidic chips. To better mimic human physiology, the chips are designed to control cell microenvironments and maintain tissue-specific functions. Combining advances in tissue engineering and microfabrication, OoCs have gained interest as a next-generation experimental platform to investigate human pathophysiology and the effect of therapeutics in the body. There are as many examples of OoCs as there are applications, making it difficult for new researchers to understand what makes one OoC more suited to an application than another. This Primer is intended to give an introduction to the aspects of OoC that need to be considered when developing an application-specific OoC. The Primer covers guiding principles and considerations to design, fabricate and operate an OoC, as well as subsequent assaying techniques to extract biological information from OoC devices. Alongside this is a discussion of current and future applications of OoC technology, to inform design and operational decisions during the implementation of OoC systems. Organs-on-chips are microfluidic systems containing miniature tissues with the aim of mimicking human physiology for a range of biomedical and therapeutic applications. Leung, de Haan et al. report practical tips to inform design and operational decisions during the implementation of organ-on-a-chip systems.

A cost-effective catalyst should have a high dispersion of the active atoms, together with a controllable surface structure for the optimization of activity, selectivity, or both. We fabricated nanocages by depositing a few atomic layers of platinum (Pt) as conformal shells on palladium (Pd) nanocrystals with well-defined facets and then etching away the Pd templates. Density functional theory calculations suggest that the etching is initiated via a mechanism that involves the formation of vacancies through the removal of Pd atoms incorporated into the outermost layer during the deposition of Pt. With the use of Pd nanoscale cubes and octahedra as templates, we obtained Pt cubic and octahedral nanocages enclosed by {100} and {111} facets, respectively, which exhibited distinctive catalytic activities toward oxygen reduction.

Medicine relies on the use of pharmacologically active agents (drugs) to manage and treat disease. However, drugs are not inherently effective; the benefit of a drug is directly related to the manner by which it is administered or delivered. Drug delivery can affect drug pharmacokinetics, absorption, distribution, metabolism, duration of therapeutic effect, excretion, and toxicity. As new therapeutics (e.g., biologics) are being developed, there is an accompanying need for improved chemistries and materials to deliver them to the target site in the body, at a therapeutic concentration, and for the required period of time. In this Perspective, we provide an historical overview of drug delivery and controlled release followed by highlights of four emerging areas in the field of drug delivery: systemic RNA delivery, drug delivery for localized therapy, oral drug delivery systems, and biologic drug delivery systems. In each case, we present the barriers to effective drug delivery as well as chemical and materials advances that are enabling the field to overcome these hurdles for clinical impact.